MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in Med Image Anal 43: 98-111, 2018, Med Image Anal 36:61-78, 2017. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

中文翻译：

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Brain SegNet：用于脑部病变分割的3D局部优化网络。

MR图像（MRI）对脑部病变的精确分割对于改善癌症诊断，手术计划和结果预测非常重要。但是，从3D MRI手动准确地对脑部病变进行分割非常昂贵，耗时且容易出现用户偏见。我们提出了一个高效但概念上简单的大脑分割网络（称为Brain SegNet），这是一个3D残差框架，用于脑部病变的自动体素分割。我们的模型能够直接预测3D脑MRI中脑肿瘤或缺血性卒中区域的密集体素分割。拟议的3D分割网络每次MRI的运行时间约为0.5s-比以前的方法快50倍Med Image Anal 43：98-111，2018，Med Image Anal 36：61-78，2017.我们的模型在BRATS上进行了评估2015年脑肿瘤分割基准，